1. Field of the Invention
The present invention relates to an analysis model generating method, an analysis model generation program product, and an analysis model generating apparatus for generating, as a Bayesian network, an analysis model that shows causal relationships between variables.
2. Description of the Related Art
Conventionally, a Bayesian network is known as a method for expressing causal relationships between variables in a model. For example, it is possible to generate, as a Bayesian network, a decision-making model of respondents from questionnaire data.
To generate the decision-making model of the respondents from the questionnaire data, the methods for determining the structure of the model that are generally known include a method in which the question items in the questionnaire are directly reflected in the model and a method in which the K2 algorithm is used. For example, a method for determining the structure of a decision-making model, using the K2 algorithm has been disclosed. (For example, see JP-A 2005-107747 (KOKAI).)
However, when the method for determining the model structure mentioned above is used, the factors in the decision-making process of the respondents are not necessarily clear before the questionnaire survey is conducted. Thus, before the questionnaire survey is actually conducted, it is necessary to prepare all the elements that each have a possibility of being a factor of the decision-making process as the question items in the questionnaire. As a result, in many situations, the number of question items tends to become large.
When such a large number of question items in the questionnaire are directly introduced into a model, the model will have a large number of parameters. As a result, a large volume of data is needed for generating a reliable model, and thus this method is not very realistic.
To solve this problem, it is acceptable to select only variables having a strong causal relationship, using the K2 algorithm, and to introduce important question items in the questionnaire into a decision-making model. However, this method also has a problem because there is a possibility of missing information that is included in some question items in the questionnaire that have a weak causal relationship and have not been introduced into the decision-making model.